Gastos Casa
12 May, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0039e+09 2 5.1491 0.006 **
## lag_depvar 2.6287e+11 1 2696.4694 <2e-16 ***
## Residuals 8.1401e+10 835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1825.235 16282.91 0.1467164
## 2-0 31393.306 23245.767 39540.84 0.0000000
## 2-1 24164.467 19448.396 28880.54 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
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## 776 40161.71 2 36232.14
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## 779 39252.29 2 45663.71
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## 782 44650.71 2 39438.43
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## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
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## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
## 831 61375.29 2 60626.86
## 832 53710.86 2 61375.29
## 833 55795.57 2 53710.86
## 834 55130.14 2 55795.57
## 835 57700.14 2 55130.14
## 836 61333.14 2 57700.14
## 837 59230.71 2 61333.14
## 838 49195.00 2 59230.71
## 839 55436.43 2 49195.00
## 840 50353.14 2 55436.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 683 53627.57 22091.176
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2018.75467 4040.31561 -537.98099 2438.15035 -2969.45204 518.81136
## 8 9 10 11 12 13
## -5655.75231 -1187.80602 -3966.13252 -417.98090 -4939.71464 -1609.41835
## 14 15 16 17 18 19
## -899.74433 377.51733 -3242.78287 -377.80026 -2129.96567 6604.28688
## 20 21 22 23 24 25
## -1529.15181 -1208.27771 1475.61382 -1186.61211 234.63187 1695.00269
## 26 27 28 29 30 31
## -7102.01296 947.67561 8192.64621 418.64068 -13.46253 -2399.97936
## 32 33 34 35 36 37
## 1576.85052 4573.21941 1127.72494 2392.32323 -1867.04906 4608.91124
## 38 39 40 41 42 43
## 4304.33764 -2274.84395 -2980.65631 -1109.44993 -10740.83104 7289.85511
## 44 45 46 47 48 49
## 2557.70098 1367.24294 8105.52382 686.51573 6529.10508 6714.75277
## 50 51 52 53 54 55
## -5881.95985 -4795.09396 -5059.40802 -7928.27117 6130.07123 -4076.36759
## 56 57 58 59 60 61
## -4894.12288 3855.96615 887.96062 -32.01797 142.29883 -4996.37715
## 62 63 64 65 66 67
## 18126.81488 3640.73409 -3645.99846 5925.51951 7344.28641 14639.24755
## 68 69 70 71 72 73
## 1693.72801 -13212.08254 -1305.38956 4644.31455 -4899.47278 -4403.67327
## 74 75 76 77 78 79
## -10496.51067 2468.06961 -5398.49563 1065.17873 -6864.88022 548.46541
## 80 81 82 83 84 85
## -2353.07872 -2692.33976 -3930.30708 -535.86697 2316.21397 3764.07304
## 86 87 88 89 90 91
## 477.99046 -483.64422 197.38841 4302.58742 -1162.60983 1151.23655
## 92 93 94 95 96 97
## -2064.17145 -1043.95149 177.93113 275.09092 -7483.77048 2392.38795
## 98 99 100 101 102 103
## -8601.95670 -2939.49538 -4038.09771 -1735.01589 -1259.44550 3182.93994
## 104 105 106 107 108 109
## -2340.35658 2595.73115 -1156.98295 971.83180 2588.29057 -3153.44912
## 110 111 112 113 114 115
## -4722.00512 -848.99326 1904.77747 11694.61150 -1242.27644 2668.91494
## 116 117 118 119 120 121
## 4263.10248 3502.77168 -1099.93955 -4716.15751 -3723.56998 2320.55845
## 122 123 124 125 126 127
## -1731.96902 1341.22908 8858.98782 847.38598 130.75792 -2520.89734
## 128 129 130 131 132 133
## 2655.62788 7052.97400 1012.56544 -8499.25545 1749.85118 4136.05801
## 134 135 136 137 138 139
## -3163.62971 -1419.21432 -853.37593 -3879.35766 1183.90979 -494.70566
## 140 141 142 143 144 145
## -2912.82821 1719.07605 -1880.31347 -7828.46824 2040.68678 -3478.70908
## 146 147 148 149 150 151
## 2103.32513 -256.64054 1023.76172 -358.73086 1352.70356 1186.99076
## 152 153 154 155 156 157
## 3356.81599 -4861.77417 -1174.15951 -3235.44434 5957.25293 9746.77867
## 158 159 160 161 162 163
## -3652.36440 -4998.47556 3384.31224 -25.19707 2476.05482 -6131.26589
## 164 165 166 167 168 169
## -6967.69248 3937.10200 17171.63989 3396.35955 -631.92750 -2679.21475
## 170 171 172 173 174 175
## -1336.29253 3358.70438 -461.90588 -8309.11008 2633.45764 4093.30901
## 176 177 178 179 180 181
## 390.87965 8515.24838 -9488.21908 -3706.66945 -10978.11279 -11468.89484
## 182 183 184 185 186 187
## 1010.54606 9066.84607 -1662.66176 5695.78790 6317.49745 12914.20325
## 188 189 190 191 192 193
## 8175.10149 -4327.35032 2201.46145 10101.53572 -1920.38325 -2720.55251
## 194 195 196 197 198 199
## -10554.76186 -6628.92860 973.32165 -5494.29877 -10052.12845 5136.20667
## 200 201 202 203 204 205
## -3319.78828 -1961.03385 -1051.31064 6247.47532 9626.94520 310.81447
## 206 207 208 209 210 211
## 2655.75889 2825.81699 5509.33845 12553.49040 -5979.13573 -11579.64514
## 212 213 214 215 216 217
## -5936.26202 -10850.02323 -5325.98851 1281.57059 -13257.25235 16155.82785
## 218 219 220 221 222 223
## 7557.04996 1266.29766 26424.24441 12231.09080 7026.78051 13712.89896
## 224 225 226 227 228 229
## -4239.64254 -2057.73862 3467.22019 50.56659 2441.83056 8703.18097
## 230 231 232 233 234 235
## 5526.33708 -2208.23680 -2121.74244 9138.73246 -11801.32400 -7560.77688
## 236 237 238 239 240 241
## -8808.97181 -10359.42183 2830.30995 1101.32497 -8549.25401 -9232.95758
## 242 243 244 245 246 247
## 8860.10231 -8011.42160 2248.84414 -10543.85073 -4286.64131 1192.20281
## 248 249 250 251 252 253
## 768.30212 -12554.22101 3416.72061 1828.30377 3972.21910 1887.47355
## 254 255 256 257 258 259
## -1412.58044 10886.72058 20611.86367 2897.27024 -4575.15008 3812.64190
## 260 261 262 263 264 265
## -1995.81403 3440.04968 -5152.27433 -11185.50518 -5004.22091 -790.57433
## 266 267 268 269 270 271
## -5456.76489 8516.05687 -4557.22083 3917.76480 -2386.31494 4153.72320
## 272 273 274 275 276 277
## 423.89686 7016.06950 -1711.45788 11727.93193 -4902.60296 1415.00961
## 278 279 280 281 282 283
## -684.94799 7540.51905 -5381.08149 -3042.95504 -11565.41076 -2949.60283
## 284 285 286 287 288 289
## 18380.31938 7472.89750 2409.71526 -956.01739 582.51782 6075.44990
## 290 291 292 293 294 295
## 6549.32249 -19115.90566 -11436.67398 -8391.20106 9414.86351 2801.10478
## 296 297 298 299 300 301
## -1456.06848 27128.41620 9730.02582 4546.95789 9159.08060 2482.96677
## 302 303 304 305 306 307
## -1404.01291 7535.93746 -24666.03098 -3836.59125 -463.04985 -7251.28986
## 308 309 310 311 312 313
## -4233.83544 2682.49910 -9447.75516 -3460.34750 -8407.58931 1364.96266
## 314 315 316 317 318 319
## -3357.98332 1846.94659 -4293.08092 27241.58805 -1024.61667 2993.50789
## 320 321 322 323 324 325
## 10525.09440 5257.15611 32038.74095 4691.40589 -21353.71471 1448.04938
## 326 327 328 329 330 331
## 770.17445 -6799.74060 -2043.04646 -33565.45094 717.55340 -2468.10862
## 332 333 334 335 336 337
## -252.06981 -3327.35878 3933.74389 -603.93214 -7120.18690 -3265.43467
## 338 339 340 341 342 343
## -2334.58653 -7819.86739 3730.04957 -1511.55536 -1879.33106 -1135.29680
## 344 345 346 347 348 349
## 33.02657 332.20031 -1774.70380 -9602.84985 -13342.50075 2211.20578
## 350 351 352 353 354 355
## -4439.16512 -3769.06466 -6087.54695 1652.98026 1270.18552 2624.30721
## 356 357 358 359 360 361
## -3914.00106 -659.65402 527.61836 6854.12793 86.58033 -233.34283
## 362 363 364 365 366 367
## 2384.63506 -2960.25236 -1078.64904 -8942.42867 -4794.04251 -6366.26031
## 368 369 370 371 372 373
## -5084.73974 -7375.50047 4910.95361 238.48000 6977.71940 -7811.59801
## 374 375 376 377 378 379
## -2413.96014 -3535.36135 -2607.00546 -12593.90191 1805.37502 -10748.50740
## 380 381 382 383 384 385
## 5611.42792 9218.02901 2962.85737 -2580.42847 1426.81456 6554.98944
## 386 387 388 389 390 391
## 11191.19158 -6069.08800 -5610.53333 -387.33568 8331.91683 1548.83098
## 392 393 394 395 396 397
## 10948.83655 -10194.38567 2497.58796 426.56470 275.75755 -940.05998
## 398 399 400 401 402 403
## -844.12714 -14763.53169 8311.23106 -1421.83177 -1605.31696 6756.83173
## 404 405 406 407 408 409
## -8183.94356 -1512.26324 -2738.73510 -6013.36885 -3027.59087 -4074.29790
## 410 411 412 413 414 415
## -8898.31090 6022.29055 1496.58167 -7529.84309 -7823.03945 14115.25430
## 416 417 418 419 420 421
## 3639.49776 4291.41644 -8260.46515 -4935.80931 -2773.45470 2656.77885
## 422 423 424 425 426 427
## -14188.60442 -2913.36774 -9214.90029 2927.61276 6868.95541 6428.21611
## 428 429 430 431 432 433
## -4169.38173 -4289.32417 -4876.90594 -1928.92529 -5849.21431 -6746.40775
## 434 435 436 437 438 439
## -6049.75917 -1478.46408 -938.01097 -5072.37920 2494.47674 4730.46273
## 440 441 442 443 444 445
## -5195.28262 -2282.97433 1452.85564 -3974.71440 2707.16515 -6725.15551
## 446 447 448 449 450 451
## -12236.38657 -4596.09124 9568.17482 -2157.67593 4630.33636 -6018.74073
## 452 453 454 455 456 457
## -1252.90944 252.44049 2887.88024 -12423.61649 3257.45078 -6833.13882
## 458 459 460 461 462 463
## 6410.37847 2868.11199 2346.71337 -4018.86104 1932.57811 -178.39774
## 464 465 466 467 468 469
## 1619.99071 -702.86842 3169.72484 -2833.74851 5621.58045 -7147.56665
## 470 471 472 473 474 475
## -3139.87726 -2368.44668 -4818.47344 2858.48891 7644.89892 -6201.34500
## 476 477 478 479 480 481
## 1323.72706 -6345.88576 -2988.41207 1876.36181 -13076.25639 -9857.05842
## 482 483 484 485 486 487
## -1276.09976 -59.01332 -1051.02394 -1435.86196 -9682.09939 11024.15928
## 488 489 490 491 492 493
## 6113.61024 7270.10216 -5617.13000 5206.31398 9110.38174 5837.02119
## 494 495 496 497 498 499
## -13709.58582 -10746.98013 -3582.17175 -1236.37850 -653.99173 -7757.02895
## 500 501 502 503 504 505
## 504.67481 4173.95355 5374.66939 503.27497 -80.10092 -7400.70640
## 506 507 508 509 510 511
## 433.81077 -5189.11618 1707.54745 -1431.68992 -8291.39478 -709.87736
## 512 513 514 515 516 517
## -2785.68452 -694.79848 1221.43250 -9616.18789 -7857.69011 24213.47839
## 518 519 520 521 522 523
## 9656.86758 5674.89233 -5559.02900 2597.86975 16810.58500 11206.57620
## 524 525 526 527 528 529
## -24449.51819 -5262.85234 -3915.21889 4404.70998 -541.08372 -11284.94332
## 530 531 532 533 534 535
## 4249.79618 13749.64286 -5188.11104 4182.67314 5348.88235 -2014.54774
## 536 537 538 539 540 541
## -4754.73984 -7267.89628 -2266.40659 8164.49712 -61.34187 -8328.81098
## 542 543 544 545 546 547
## 1659.53504 -763.36183 204.32230 -11194.62788 -11186.38707 1951.36933
## 548 549 550 551 552 553
## 6899.63416 -1452.43455 703.53219 -7860.41648 8449.86042 756.08842
## 554 555 556 557 558 559
## -12100.22281 9046.70246 8503.31707 -89.40487 4664.46143 -3780.15716
## 560 561 562 563 564 565
## 13917.32772 21257.16507 -6781.21640 -9960.68706 6538.98851 -29.00266
## 566 567 568 569 570 571
## 3205.72417 -7631.77827 -17527.52987 6512.57681 6261.36330 1708.08229
## 572 573 574 575 576 577
## 2900.87837 1565.30572 -2371.54491 14522.64528 -9893.47130 -6448.70134
## 578 579 580 581 582 583
## 8532.59356 2649.99433 -6760.25399 7321.93305 -4011.16127 -2968.97970
## 584 585 586 587 588 589
## 15522.10348 -14733.76531 8250.36601 -135.53386 -6414.16668 -929.55133
## 590 591 592 593 594 595
## 81.54040 -10822.74163 1666.46178 -7282.81088 2953.09361 8737.62569
## 596 597 598 599 600 601
## -7662.93716 5715.36355 2576.27219 6687.34462 -3381.74502 5972.05450
## 602 603 604 605 606 607
## -8495.26033 2091.23644 1099.28317 2964.95868 1313.11866 213.15474
## 608 609 610 611 612 613
## -5992.87412 7915.62202 -1369.23661 -2751.64736 -3618.53106 -8379.17598
## 614 615 616 617 618 619
## 11836.80407 4772.61760 -9492.89024 11482.60027 5867.66153 -5771.31456
## 620 621 622 623 624 625
## 26186.17562 -13079.85897 -6978.43071 2987.77474 -4335.14284 -10750.41317
## 626 627 628 629 630 631
## 11174.86447 -21794.40725 -2503.94799 8589.15503 11017.53717 -1707.18905
## 632 633 634 635 636 637
## 33136.30855 -6836.92425 5499.53346 5172.32085 -2502.53897 -5561.62883
## 638 639 640 641 642 643
## -2131.25290 -12610.11662 -2378.78367 -2015.39865 -2644.02608 -2974.90284
## 644 645 646 647 648 649
## 1705.27141 4313.23469 16831.98453 18368.05344 645.90788 4558.09415
## 650 651 652 653 654 655
## 10375.31121 19892.43242 443.25634 -28346.45198 -1523.56283 -2460.94721
## 656 657 658 659 660 661
## 1712.83962 -3346.26619 -10760.90044 1558.99635 4116.57727 -1127.36812
## 662 663 664 665 666 667
## 12915.84384 1210.20656 1663.97527 -11841.07486 1263.62065 1069.50486
## 668 669 670 671 672 673
## -5285.14604 -7513.93528 1982.46335 -3802.15894 2591.25598 -3469.54350
## 674 675 676 677 678 679
## -9420.23135 -8370.61134 -3029.70114 119.65274 2786.60316 640.60990
## 680 681 682 683 684 685
## -3904.84616 -1881.70450 -1391.58439 -8316.92637 4587.98201 -2318.78042
## 686 687 688 689 690 691
## -1473.49089 511.43600 10772.72291 9742.75492 10496.82748 -9807.16910
## 692 693 694 695 696 697
## -3670.53436 -3245.85981 5772.81504 -10494.94797 -7997.49349 -8682.19387
## 698 699 700 701 702 703
## -6331.63716 -4789.67567 3034.40892 -4461.73256 -1954.75225 4163.94841
## 704 705 706 707 708 709
## 31036.22131 9417.28970 23343.28609 1577.04363 8229.83961 22833.30335
## 710 711 712 713 714 715
## 6476.17504 -18278.37609 4764.60479 -5497.95910 -150.10226 431.82496
## 716 717 718 719 720 721
## -17313.64135 -5305.41191 3295.12682 -3054.34438 -13018.35587 4243.63081
## 722 723 724 725 726 727
## -5594.37700 705.49921 -3972.60192 -12484.55783 1333.47426 -1902.77246
## 728 729 730 731 732 733
## -9813.63378 17234.94012 1734.53842 -2764.34516 5674.33500 -8673.41772
## 734 735 736 737 738 739
## -763.43224 8098.21235 -15394.46241 -5949.06426 7371.28167 -4824.62440
## 740 741 742 743 744 745
## 121.78468 1788.03440 -1996.61457 -5208.52372 6373.42078 -6316.73003
## 746 747 748 749 750 751
## 22658.78002 7780.42918 -1994.93831 -7334.57724 23372.95316 -4339.40870
## 752 753 754 755 756 757
## 1351.14608 -14466.33800 56069.18759 26875.51049 15053.05479 -10683.91146
## 758 759 760 761 762 763
## 10574.88833 7284.50865 5781.41787 -46415.42587 -16191.72487 946.78964
## 764 765 766 767 768 769
## -2537.39347 -3477.70911 122824.12832 19301.62583 43713.97860 22585.22539
## 770 771 772 773 774 775
## 12078.47625 15850.14183 25730.98864 -98803.58752 -6765.82116 -35841.92504
## 776 777 778 779 780 781
## 1721.94675 -1243.79836 3380.96834 -7429.33726 -1460.37252 -1960.59713
## 782 783 784 785 786 787
## 3409.10789 -7169.69040 -2251.85469 3904.46418 2251.43403 -2772.11251
## 788 789 790 791 792 793
## -4060.28681 1725.98076 2841.27462 -62.62740 -6714.16835 -5793.85350
## 794 795 796 797 798 799
## -1159.21320 -1274.89507 -7828.96619 -2366.53520 -3280.87478 -2678.46832
## 800 801 802 803 804 805
## 10711.31736 2230.10678 7069.17396 2914.22743 -5456.83940 8178.44879
## 806 807 808 809 810 811
## 9867.00637 -10608.31379 -7403.57475 -7512.87711 2997.60546 4186.58529
## 812 813 814 815 816 817
## -2276.56254 -14171.97130 -4118.85781 6251.05116 8229.49139 -9679.66579
## 818 819 820 821 822 823
## -7757.14951 -9343.77577 9745.74793 -1230.10737 -4378.90981 -8454.30147
## 824 825 826 827 828 829
## 7866.77759 7906.91824 4969.03998 -3108.51927 -718.28784 2885.51476
## 830 831 832 833 834 835
## 4662.11434 1617.99859 -6700.45038 2081.88718 -405.28593 2746.20424
## 836 837 838 839 840
## 4133.38883 -1143.76636 -9342.25633 5668.96284 -4868.44602
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17250.53 20098.68 24354.12 24071.99 26426.17 23757.90 24474.47 19704.95
## 10 11 12 13 14 15 16 17
## 19441.42 16783.27 17561.00 14289.28 14340.46 15005.34 16702.50 15021.94
## 18 19 20 21 22 23 24 25
## 16056.97 15430.28 22515.15 21598.85 21078.53 22969.18 22294.94 22947.71
## 26 27 28 29 30 31 32 33
## 24794.30 18720.61 20447.35 28287.36 28345.03 28017.84 25646.44 27049.35
## 34 35 36 37 38 39 40 41
## 30893.70 31242.25 32651.91 30161.66 34138.66 37347.84 34402.94 31212.74
## 42 43 44 45 46 47 48 49
## 30060.12 20636.43 28157.73 30595.04 31684.62 38525.06 38019.47 42683.25
## 50 51 52 53 54 55 56 57
## 46920.96 39616.38 34182.98 29203.99 22346.07 28638.22 25217.69 21514.03
## 58 59 60 61 62 63 64 65
## 25923.90 27183.88 27480.99 27892.95 23762.47 40359.41 42204.00 37448.34
## 66 67 68 69 70 71 72 73
## 41656.71 46574.04 57245.84 55258.94 40497.10 38002.11 41021.04 35319.24
## 74 75 76 77 78 79 80 81
## 30769.94 21470.22 24672.78 20597.11 22683.88 17577.68 19593.79 18820.05
## 82 83 84 85 86 87 88 89
## 17847.45 15915.72 17193.93 20803.21 25222.44 26212.64 26237.61 26854.56
## 90 91 92 93 94 95 96 97
## 30981.04 29811.19 30810.89 28874.67 28074.21 28442.48 28849.20 22424.47
## 98 99 100 101 102 103 104 105
## 25440.53 18468.64 17324.38 15364.44 15664.30 16341.92 20816.07 19899.27
## 106 107 108 109 110 111 112 113
## 23411.55 23201.45 24878.14 27755.88 25253.15 21695.42 21970.94 24618.10
## 114 115 116 117 118 119 120 121
## 35486.28 33678.51 35516.61 38515.94 40472.51 38160.16 32979.43 29319.58
## 122 123 124 125 126 127 128 129
## 31403.11 29682.49 30864.44 38466.76 38109.10 37170.33 34032.80 35814.60
## 130 131 132 133 134 135 136 137
## 41214.29 40654.40 31853.15 33118.37 36309.20 32718.64 31105.38 30190.07
## 138 139 140 141 142 143 144 145
## 26745.95 28160.85 27930.40 25615.92 27641.03 26265.33 19865.31 22896.85
## 146 147 148 149 150 151 152 153
## 20722.82 23700.93 24241.10 25832.02 26014.15 27668.87 28970.04 32003.20
## 154 155 156 157 158 159 160 161
## 27471.87 26734.59 24289.03 30185.08 41672.79 40002.48 37366.54 42388.48
## 162 163 164 165 166 167 168 169
## 43797.52 47214.55 42678.98 37984.61 43411.65 59719.21 61932.07 60345.64
## 170 171 172 173 174 175 176 177
## 57170.29 55569.01 58272.48 57296.25 49585.83 52410.26 56154.12 56190.32
## 178 179 180 181 182 183 184 185
## 63321.50 53820.67 50570.54 41376.18 32912.74 36422.15 46528.95 45984.78
## 186 187 188 189 190 191 192 193
## 51939.50 57686.37 68472.90 73757.49 67450.11 67643.61 74716.24 70391.27
## 194 195 196 197 198 199 200 201
## 65912.62 55152.93 49181.11 50605.87 46199.13 38365.36 44792.22 43019.03
## 202 203 204 205 206 207 208 209
## 42656.88 43135.38 49931.63 58823.76 58453.24 60178.61 61834.95 65627.37
## 210 211 212 213 214 215 216 217
## 75096.99 67177.22 55362.40 49969.45 40962.85 37919.57 41034.25 31051.17
## 218 219 220 221 222 223 224 225
## 48030.24 55353.42 56255.61 79028.48 86525.93 88529.82 96123.64 87071.60
## 226 227 228 229 230 231 232 233
## 81068.07 80649.86 77298.74 76459.96 81198.52 82563.24 76996.89 72208.27
## 234 235 236 237 238 239 240 241
## 77863.75 64507.21 56541.11 48489.14 40097.98 44291.25 46444.68 39893.24
## 242 243 244 245 246 247 248 249
## 33570.75 43856.56 38101.58 42038.57 34299.93 33005.37 36661.84 39486.65
## 250 251 252 253 254 255 256 257
## 30313.14 36253.12 40055.78 45252.24 47971.44 47463.85 57768.14 75271.02
## 258 259 260 261 262 263 264 265
## 75086.01 68394.50 69876.81 66096.38 67542.99 61298.65 50569.79 46595.86
## 266 267 268 269 270 271 272 273
## 46805.34 42910.80 51717.79 47989.66 52137.74 50253.71 54322.39 54618.50
## 274 275 276 277 278 279 280 281
## 60637.89 58271.35 67947.46 61870.28 62080.38 60428.91 66173.65 59902.10
## 282 283 284 285 286 287 288 289
## 56464.84 46013.75 44409.97 61647.82 67179.71 67589.30 65006.05 64093.12
## 290 291 292 293 294 295 296 297
## 68095.39 72006.91 52997.25 43096.06 37105.14 47429.90 50672.78 49786.44
## 298 299 300 301 302 303 304 305
## 73990.69 79938.04 80605.92 85219.89 83417.87 78446.49 81914.46 56805.02
## 306 307 308 309 310 311 312 313
## 53064.91 52744.58 46532.69 43741.22 47345.76 39895.49 38617.16 33176.89
## 314 315 316 317 318 319 320 321
## 36962.70 36143.77 39976.51 37960.27 63755.19 61595.63 63219.76 71220.56
## 322 323 324 325 326 327 328 329
## 73608.69 99098.88 97476.00 73298.09 72095.54 70452.31 62401.33 59522.59
## 330 331 332 333 334 335 336 337
## 29460.88 33149.68 33589.36 35910.07 35250.68 41019.65 42095.62 37341.58
## 338 339 340 341 342 343 344 345
## 36555.73 36682.44 31999.81 38000.84 38664.47 38923.01 39799.12 41585.66
## 346 347 348 349 350 351 352 353
## 43408.28 43159.85 36102.07 26666.65 32013.17 30873.78 30463.69 28079.31
## 354 355 356 357 358 359 360 361
## 32759.81 36515.41 40980.57 39168.94 40429.67 42568.87 49966.71 50517.49
## 362 363 364 365 366 367 368 369
## 50719.22 53183.25 50665.79 50110.14 42752.76 39948.55 36124.17 33902.07
## 370 371 372 373 374 375 376 377
## 29958.47 37248.95 39536.71 47425.03 41394.53 40841.50 39378.29 38910.90
## 378 379 380 381 382 383 384 385
## 29775.34 34375.08 27424.29 35646.54 45983.29 49550.00 47822.76 49815.15
## 386 387 388 389 390 391 392 393
## 56037.52 65526.37 58735.25 53201.48 52930.08 60312.31 60835.88 69507.67
## 394 395 396 397 398 399 400 401
## 58609.41 60176.86 59736.81 59220.49 57706.84 56467.96 43221.77 51810.55
## 402 403 404 405 406 407 408 409
## 50810.60 49776.45 56180.09 48719.83 48030.74 46356.80 42032.45 40862.73
## 410 411 412 413 414 415 416 417
## 38925.88 33017.85 40893.56 43820.99 38491.33 33577.75 48454.93 52301.15
## 418 419 420 421 422 423 424 425
## 56231.89 48698.24 45020.17 43695.65 47283.46 35698.22 35427.33 29683.96
## 426 427 428 429 430 431 432 433
## 35275.90 43606.64 50501.38 47265.61 44333.19 41257.21 41145.36 37621.84
## 434 435 436 437 438 439 440 441
## 33758.76 30991.75 32568.44 34418.52 32422.38 37290.39 43498.28 40249.40
## 442 443 444 445 446 447 448 449
## 39955.29 42962.86 40848.12 44839.16 40084.24 31113.09 29950.11 41311.39
## 450 451 452 453 454 455 456 457
## 40992.81 46646.17 42280.62 42630.42 44251.55 47971.19 37841.55 42692.71
## 458 459 460 461 462 463 464 465
## 38114.19 45686.17 49207.57 51829.15 48557.42 50899.11 51100.72 52848.44
## 466 467 468 469 470 471 472 473
## 52345.85 55290.75 52617.99 57671.14 50928.45 48538.45 47124.04 43747.08
## 474 475 476 477 478 479 480 481
## 47504.67 54970.92 49395.70 51099.60 45886.41 44264.78 47098.83 36508.92
## 482 483 484 485 486 487 488 489
## 30067.96 31938.01 34635.74 36126.29 37092.53 30730.84 43265.96 49928.75
## 490 491 492 493 494 495 496 497
## 56761.70 51471.11 56306.05 63942.69 67755.59 54006.55 44580.74 42604.95
## 498 499 500 501 502 503 504 505
## 42928.28 43719.74 38204.33 40604.19 45907.76 51591.58 52301.53 52412.13
## 506 507 508 509 510 511 512 513
## 46111.62 47452.12 43709.88 46466.40 46131.97 39845.31 40976.83 40151.66
## 514 515 516 517 518 519 520 521
## 41257.71 43898.76 36736.12 32013.66 55912.56 64076.39 67730.74 61107.27
## 522 523 524 525 526 527 528 529
## 62447.27 76038.14 83017.52 57958.14 52826.22 49519.29 53899.94 53406.09
## 530 531 532 533 534 535 536 537
## 43585.92 48579.64 61244.97 55763.76 59162.69 63151.98 60203.45 55232.32
## 538 539 540 541 542 543 544 545
## 48692.12 47347.50 55287.63 55037.95 47595.18 49819.65 49646.25 50340.34
## 546 547 548 549 550 551 552 553
## 40985.82 32818.49 37161.94 45281.58 45078.47 46784.99 40792.57 49808.91
## 554 555 556 557 558 559 560 561
## 50964.65 40740.01 50284.54 58150.26 57514.97 61114.01 56879.67 68644.55
## 562 563 564 565 566 567 568 569
## 85339.36 75426.69 63986.01 68406.86 66530.56 67717.64 59284.53 43267.71
## 570 571 572 573 574 575 576 577
## 50278.92 56186.20 57369.41 59445.69 60092.97 57218.35 69469.47 58838.99
## 578 579 580 581 582 583 584 585
## 52559.69 60164.01 61668.54 54760.07 61028.88 56603.41 53646.90 67221.91
## 586 587 588 589 590 591 592 593
## 52645.21 59992.11 59084.17 52804.12 52109.03 52385.17 43097.68 45895.53
## 594 595 596 597 598 599 600 601
## 40520.05 44767.37 53533.79 46862.64 52723.73 55102.37 60773.46 56930.23
## 602 603 604 605 606 607 608 609
## 61745.69 53311.33 55192.00 55968.61 58277.60 58851.85 58392.45 52567.81
## 610 611 612 613 614 615 616 617
## 59631.95 57691.36 54787.53 51492.46 44452.91 55967.24 59856.03 50788.26
## 618 619 620 621 622 623 624 625
## 61193.91 65380.31 58867.82 81103.14 66220.72 58547.37 60551.00 55902.70
## 626 627 628 629 630 631 632 633
## 46234.71 56945.84 37495.38 37355.56 46927.18 57413.47 55457.41 84196.35
## 634 635 636 637 638 639 640 641
## 74379.18 76580.68 78218.54 72943.06 65659.82 62292.97 50193.78 48561.54
## 642 643 644 645 646 647 648 649
## 47452.74 45934.47 44318.59 46996.34 51615.30 66591.23 81020.38 78142.76
## 650 651 652 653 654 655 656 657
## 79046.83 84920.28 98369.46 93126.31 63386.42 60837.38 57790.73 58775.69
## 658 659 660 661 662 663 664 665
## 55215.47 45625.00 48010.14 52329.37 51521.30 63086.94 62964.60 63254.22
## 666 667 668 669 670 671 672 673
## 51705.81 53065.78 54084.57 49421.79 43399.54 46435.44 44033.46 47521.40
## 674 675 676 677 678 679 680 681
## 45273.09 38108.33 32764.56 32762.06 35511.97 40245.53 42506.70 40510.56
## 682 683 684 685 686 687 688 689
## 40534.16 40983.07 35323.59 41655.07 41152.35 41451.71 43447.85 54159.10
## 690 691 692 693 694 695 696 697
## 62619.17 70671.03 59964.39 55970.86 52852.18 58007.95 48297.64 41994.62
## 698 699 700 701 702 703 704 705
## 35888.35 32606.39 31085.88 36594.30 34857.32 35530.19 41465.06 70133.85
## 706 707 708 709 710 711 712 713
## 76294.43 93847.24 90165.30 92761.41 107791.40 106631.66 83986.25 84333.67
## 714 715 716 717 718 719 720 721
## 75669.25 72771.03 70746.93 53471.13 48868.02 52361.20 49865.21 38976.94
## 722 723 724 725 726 727 728 729
## 44546.66 40816.79 43062.60 40937.13 31641.53 35593.49 36218.92 29852.49
## 730 731 732 733 734 735 736 737
## 47925.75 50174.06 48207.38 53862.99 46267.29 46541.93 54525.75 40973.21
## 738 739 740 741 742 743 744 745
## 37384.15 45887.91 42661.50 44164.54 46934.04 46046.95 42465.01 49455.87
## 746 747 748 749 750 751 752 753
## 44475.51 65443.86 70765.65 66873.86 58806.90 78591.55 71663.85 70582.77
## 754 755 756 757 758 759 760 761
## 55815.81 104549.63 121624.95 126215.20 107735.97 110164.92 109412.15 107440.85
## 762 763 764 765 766 767 768 769
## 60105.58 45152.50 47062.25 45686.42 43662.44 152263.66 156701.74 181912.92
## 770 771 772 773 774 775 776 777
## 185480.38 179416.43 177413.30 184297.30 81487.39 72074.07 38439.77 41873.66
## 778 779 780 781 782 783 784 785
## 42282.75 46681.62 41078.94 41399.03 41241.61 45796.40 40532.28 40229.68
## 786 787 788 789 790 791 792 793
## 45344.99 48370.54 46624.57 43973.16 46712.58 50081.06 50487.03 45029.28
## 794 795 796 797 798 799 800 801
## 41064.21 41649.32 42059.54 36690.68 36772.45 36044.90 35935.54 47540.75
## 802 803 804 805 806 807 808 809
## 50270.68 56884.92 59033.98 53596.84 60760.85 68496.74 57364.29 50436.59
## 810 811 812 813 814 815 816 817
## 44287.25 48098.27 52467.56 50637.83 38644.00 36948.09 44527.94 52880.52
## 818 819 820 821 822 823 824 825
## 44529.44 38911.78 32616.25 43796.39 43974.91 41379.30 35549.79 44717.94
## 826 827 828 829 830 831 832 833
## 52764.67 57229.09 54071.72 53401.34 55964.74 59757.29 60411.31 53713.68
## 834 835 836 837 838 839 840
## 55535.43 54953.94 57199.75 60374.48 58537.26 49767.47 55221.59
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8102
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.149055 0.7793924 3.893868
## t2* 2696.469396 169.2176680 897.884015
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.101622 5.160809 13.39701
## 2 lag_depvar 1641.420957 2736.931326 4550.39893Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon May 12 00:59:38 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon May 12 00:59:48 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon May 12 00:59:57 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon May 12 01:00:07 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon May 12 01:00:16 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon May 12 01:00:26 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon May 12 01:00:36 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon May 12 01:00:45 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon May 12 01:00:55 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon May 12 01:01:05 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 10.09225 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 312.85525 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 56.21325 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 3.29750 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.00000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 41.43750 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 26.12225 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 16.49500 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.00000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.00000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 466.51300 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2716, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2716 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-06-09 00:04:58 sería de: 26.669 pesos// Percentil 95% más alto proyectado: 35.110,81
Según TimeGPT: La proyección de la UF a 298 días más 2026-04-03 sería de: 40.528,13 pesos// Percentil 80% más alto proyectado: 40.841,38 pesos// Percentil 95% más alto proyectado: 41.156,83
Según prophet: La proyección de la UF a 298 días más 2026-04-03 sería de: 41.051 pesos// Percentil 95% más alto proyectado: 46.315
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26284.61 | 26323.83 |
| Lo.80 | 26417.00 | 26488.31 |
| Point.Forecast | 26668.91 | 26799.01 |
| Hi.80 | 31482.21 | 32162.56 |
| Hi.95 | 34372.51 | 35001.85 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5517 -0.2958
## s.e. 0.1178 0.1321
##
## sigma^2 = 37898: log likelihood = -494.63
## AIC=995.27 AICc=995.61 BIC=1002.18paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.3686 591.0263 14.1117
## s.e. 0.0989 262.2242 8.0260
##
## sigma^2 = 36262: log likelihood = -498.66
## AIC=1005.31 AICc=1005.89 BIC=1014.58forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 678.4072 | 646.2154 | 701.4202 |
| Lo.80 | 816.0921 | 790.9497 | 796.5527 |
| Point.Forecast | 1076.1852 | 1064.3594 | 1012.6735 |
| Hi.80 | 1336.2783 | 1363.0738 | 1287.0884 |
| Hi.95 | 1473.9633 | 1521.2037 | 1461.1317 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.3 data.table_1.17.0
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.2
## [4] httr2_1.1.2 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.17.0 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.2.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.10 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.2 askpass_1.2.1 pkgbuild_1.4.7
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 data.tree_1.1.0 tokenizers_0.3.0
## [31] listenv_0.9.1 anytime_0.3.11 performance_0.13.0
## [34] spatial_7.3-17 parallelly_1.43.0 codetools_0.2-20
## [37] xml2_1.3.8 tidyselect_1.2.1 ggeffects_2.2.1
## [40] farver_2.1.2 urca_1.3-4 its.analysis_1.6.0
## [43] matrixStats_1.5.0 stats4_4.4.0 jsonlite_2.0.0
## [46] ellipsis_0.3.2 Formula_1.2-5 iterators_1.0.14
## [49] systemfonts_1.2.2 foreach_1.5.2 tools_4.4.0
## [52] glue_1.8.0 xfun_0.52 TTR_0.24.4
## [55] ggfortify_0.4.17 loo_2.8.0 withr_3.0.2
## [58] timeSeries_4041.111 fastmap_1.2.0 boot_1.3-30
## [61] openssl_2.3.2 caTools_1.18.3 digest_0.6.37
## [64] timechange_0.3.0 R6_2.6.1 lfe_3.1.1
## [67] colorspace_2.1-1 networkD3_0.4.1 gtools_3.9.5
## [70] generics_0.1.3 htmlwidgets_1.6.4 ggstats_0.9.0
## [73] pkgconfig_2.0.3 gtable_0.3.6 timeDate_4041.110
## [76] lmtest_0.9-40 selectr_0.4-2 janeaustenr_1.0.0
## [79] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [82] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [85] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [88] curl_6.2.2 cachem_1.1.0 sjlabelled_1.2.0
## [91] KernSmooth_2.23-22 parallel_4.4.0 fBasics_4041.97
## [94] pillar_1.10.2 vctrs_0.6.5 gplots_3.2.0
## [97] slam_0.1-55 car_3.1-3 dbplyr_2.5.0
## [100] xtable_1.8-4 evaluate_1.0.3 mvtnorm_1.3-3
## [103] cli_3.6.5 compiler_4.4.0 crayon_1.5.3
## [106] rngtools_1.5.2 future.apply_1.11.3 labeling_0.4.3
## [109] sjmisc_2.8.10 rstan_2.32.7 QuickJSR_1.7.0
## [112] viridisLite_0.4.2 assertthat_0.2.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.40.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.27 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))